code: def testOutputShape(self, network: nn.Module):
      kwargs = {}
      if network in [
          networks.FullRainbowNetwork,
          networks.RainbowNetwork,
          networks.ClassicControlRainbowNetwork,
          networks.QuantileNetwork,
      ]:
          q_network = network(
              num_actions=self._num_actions, num_atoms=self._num_atoms
          )
          if network != networks.QuantileNetwork:
              kwargs = {"support": self._support}
      else:
          q_network = network(num_actions=self._num_actions)
  
      x = onp.ones(self._input_shape)
      params = q_network.init(self._rng, x=x, **kwargs)
  
      def get_q_values(states):
          return q_network.apply(params, states, **kwargs).q_values
  
      get_q_values_batch = jax.vmap(get_q_values, in_axes=(0,))
      onp.testing.assert_equal(
          get_q_values(x).shape[0], self._num_actions
      )
      batch_q_values = get_q_values_batch(
          onp.ones(self._batch_input_shape)
      )
      onp.testing.assert_equal(
          batch_q_values.shape, (self._batch_size, self._num_actions)
      )
  
file_path: dopamine/tests/dopamine/jax/networks_test.py
imports: from absl.testing import absltest
  from absl.testing import parameterized
  from dopamine.jax import networks
  from flax import linen
  import jax
  import numpy
label_desc: Use image node_id 2 for calling the NetworksTest obj's underlying member method code with example usage: obj.testOutputShape(network) and returns: q_network